Augmenting Mobility Safety in Cities by Increasing Data Pools from Connected Urban Devices
Dixit A, Allam Z (2023)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2023
Publisher: Springer
Edited Volumes: Sustainable Urban Transitions
Series: Urban Sustainability
Book Volume: Part F3685
Pages Range: 363-373
DOI: 10.1007/978-981-99-2695-4_21
Abstract
While cities pursue transitions to ‘smarter’ pathways, the use of technology within urban quarters is gaining in popularity, including for rendering safer mobility. Specifically, Machine Learning (ML) is revolutionizing the automotive industry and the Artificial Intelligence (AI) being applied to almost every stage of development of automobiles. While development stages can be time consuming, the use of AI and ML powered simulations can help in increasing the efficiency of design stages, while reducing the amount of computational and human resource dependency, hence enabling faster results with more accuracy leading to faster development cycles and vehicle roll outs. Taking the idea further is the fact that Autonomous Vehicles (AVs), are currently restricted to using data sourced from the vehicles and omit the data-rich landscapes that surround them in within ‘smarter’ cities. Through this chapter, we argue that the development of AVs can gain from AI and ML tools, along with the development of tools that can integrate data sourced from third party service providers. Doing this will help in increasing efficiency of driving as well as the safety of passengers and urban dwellers.
Involved external institutions
France (FR)How to cite
APA:
Dixit, A., & Allam, Z. (2023). Augmenting Mobility Safety in Cities by Increasing Data Pools from Connected Urban Devices. In Sustainable Urban Transitions. (pp. 363-373). Springer.
MLA:
Dixit, Aditya, and Zaheer Allam. "Augmenting Mobility Safety in Cities by Increasing Data Pools from Connected Urban Devices." Sustainable Urban Transitions. Springer, 2023. 363-373.
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